{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banner_Top_06.06.18.jpg?raw=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exam Scheduling with Graph Theory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "  function code_toggle() {\n",
       "    if (code_shown){\n",
       "      $('div.input').hide('500');\n",
       "      $('#toggleButton').val('Show Code')\n",
       "    } else {\n",
       "      $('div.input').show('500');\n",
       "      $('#toggleButton').val('Hide Code')\n",
       "    }\n",
       "    code_shown = !code_shown\n",
       "  }\n",
       "  \n",
       "  $( document ).ready(function(){\n",
       "    code_shown=false;\n",
       "    $('div.input').hide()\n",
       "  });\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Show Code\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running this cell displays a button to toggle hidden code\n",
    "#From: http://chris-said.io/2016/02/13/how-to-make-polished-jupyter-presentations-with-optional-code-visibility/\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "  function code_toggle() {\n",
    "    if (code_shown){\n",
    "      $('div.input').hide('500');\n",
    "      $('#toggleButton').val('Show Code')\n",
    "    } else {\n",
    "      $('div.input').show('500');\n",
    "      $('#toggleButton').val('Hide Code')\n",
    "    }\n",
    "    code_shown = !code_shown\n",
    "  }\n",
    "  \n",
    "  $( document ).ready(function(){\n",
    "    code_shown=false;\n",
    "    $('div.input').hide()\n",
    "  });\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Show Code\"></form>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook takes data containing student ID numbers and what classes they take in order to create an optimal (minimum number of days) exam schedule with no conflicts. The conditions for this schedule are:\n",
    "\n",
    "* There are two sessions for exams on each day - AM and PM.\n",
    "* No student will write two exams on the same day.\n",
    "* The exam space is limited to 150 students at any time.\n",
    "\n",
    "This notebook works with data where column 1 contains the grade of the student, column 2 contains student numbers, and column 3 contains class names.\n",
    "\n",
    "### Exam Scheduling Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'grinpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-538ec4c05161>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnetworkx\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgrinpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'grinpy'"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Setting up the tools we'll be using in this notebook:\n",
    "'''\n",
    "\n",
    "import networkx as nx\n",
    "import grinpy as gp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display, HTML\n",
    "from collections import OrderedDict\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Reading the data into the notebook:\n",
    "'''\n",
    "\n",
    "#Opening excel file of data:\n",
    "data = pd.read_excel('files/Exam Scheduling Input Data.xlsx', header = None)\n",
    "\n",
    "#Putting the data into a pandas dataframe:\n",
    "data_df = pd.DataFrame(data)\n",
    "data_df.columns =['Grade','Student ID','Class']\n",
    "\n",
    "#Displaying the data:\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Creating a dictionary that lists each student and their classes:\n",
    "'''\n",
    "\n",
    "#Getting the list of unique students:\n",
    "students = data.iloc[:, 1].tolist()\n",
    "students = set(students)\n",
    "\n",
    "#Creating a dictionary that will be in the form: {student1 : [class1, class2], student2 : [class2, class4] ...}\n",
    "student_dictionary = {}\n",
    "\n",
    "#Iterates through each row in the data:\n",
    "for row in data.itertuples():\n",
    "    \n",
    "    #If this row is a student not yet added to the dictionary:\n",
    "    if row[2] not in student_dictionary:\n",
    "        \n",
    "        #Add this student and the class for this row\n",
    "        student_dictionary[row[2]] = [row[3]]\n",
    "            \n",
    "    #If the student already has a dictionary entry:\n",
    "    else:\n",
    "        \n",
    "        #Add the class to the value list:\n",
    "        student_dictionary[row[2]].append(row[3])\n",
    "        \n",
    "#Converting student_dictionary to a pandas dataframe:\n",
    "student_data = pd.DataFrame([(k, v) for k, v in student_dictionary.items()])\n",
    "student_data.columns =['Student ID','Classes']\n",
    "\n",
    "#Displaying the dataframe:\n",
    "student_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e272099a047b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Getting the list of unique classes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Creating a dictionary that lists each class and the number of students in that class:\n",
    "'''\n",
    "\n",
    "#Getting the list of unique classes\n",
    "classes = data.iloc[:, 2].tolist()\n",
    "classes = set(classes)\n",
    "\n",
    "#Create a dictionary that will be in the form: {class1: number of students in class1, ...}\n",
    "class_dictionary = {}\n",
    "\n",
    "#Iterates through each row in the data:\n",
    "for row in data.itertuples():\n",
    "    \n",
    "    #If this row is a class not yet added to the dictionary:\n",
    "    if row[3] not in class_dictionary:\n",
    "        \n",
    "        #Add one to the number of students in this class:\n",
    "        class_dictionary[row[3]] = 1\n",
    "        \n",
    "    #If the student already has a dictionary entry:\n",
    "    else:\n",
    "        \n",
    "        #Add one to the number of students in this class:\n",
    "        class_dictionary[row[3]] += 1\n",
    "        \n",
    "#Converting class_dictionary to a pandas dataframe:\n",
    "class_data = pd.DataFrame([(k, v) for k, v in class_dictionary.items()])\n",
    "class_data.columns =['Class','Number of Students']\n",
    "\n",
    "#Displaying the dataframe:\n",
    "class_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'classes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-1f22f210979d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#Adding each unique class as a node in the graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'classes' is not defined"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Creating the conflict graph, and counting the number of nodes and edges:\n",
    "'''\n",
    "\n",
    "#Creating graph G\n",
    "G = nx.Graph()\n",
    "\n",
    "#Adding each unique class as a node in the graph\n",
    "for item in classes:\n",
    "    G.add_node(item)\n",
    "    \n",
    "#Iterates through the dictionary of students, adding conflict edges between all the classes each student has:\n",
    "for student in student_dictionary:\n",
    "    \n",
    "    #Iterates through all pairs of classes for the student:\n",
    "    for x in range(0, len(student_dictionary[student])):\n",
    "        for y in range (x+1, len(student_dictionary[student])):\n",
    "            \n",
    "            #Creates edge for conflict:\n",
    "            G.add_edge(student_dictionary[student][x], student_dictionary[student][y])\n",
    "            \n",
    "#Finding number of nodes and edges in the conflict graph\n",
    "node_count = G.number_of_nodes()\n",
    "edge_count = G.number_of_edges()\n",
    "\n",
    "G_data = {'Nodes': [node_count], 'Edges': [edge_count]}\n",
    "\n",
    "G_df = pd.DataFrame(G_data)\n",
    "\n",
    "#Displaying the dataframe:\n",
    "display(HTML(G_df.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49c1495e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Draws the conflict graph:\n",
    "'''\n",
    "\n",
    "#Each node is a class, each edge is a scheduling conflict.\n",
    "nx.draw_circular(G, with_labels=False, width = .5, node_color=range(29), node_size=800, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Finds a possible graph colouring (not final). Colors are named 0, 1, 2, etc.\n",
    "'''\n",
    "\n",
    "sample_color = nx.greedy_color(G, strategy = 'random_sequential')\n",
    "\n",
    "#Converting the colouring to a pandas dataframe:\n",
    "color_data = pd.DataFrame([(k, v) for k, v in sample_color.items()])\n",
    "color_data.columns =['Class','Colour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Science10': 0,\n",
       " 'Calculus12': 0,\n",
       " 'Physics11': 1,\n",
       " 'Chemistry11Enriched': 2,\n",
       " 'PreCalculus12': 3,\n",
       " 'PreCalculus11': 3,\n",
       " 'French9': 0,\n",
       " 'BiologyLifeSciences11Enriched': 0,\n",
       " 'Physics12': 1,\n",
       " 'Chemistry11': 2,\n",
       " 'Mathematics9': 1,\n",
       " 'FoundationsofMathematics12': 0,\n",
       " 'PreCalculus11Accelerated': 3,\n",
       " 'Chemistry12': 2,\n",
       " 'SocialStudies11': 4,\n",
       " 'Science9': 2,\n",
       " 'Science10Enriched': 0,\n",
       " 'FoundationsofMathematics11': 0,\n",
       " 'PreCalculus12Accelerated': 3,\n",
       " 'Spanish9': 4,\n",
       " 'English9': 5,\n",
       " 'Law12': 4,\n",
       " 'BiologyLifeSciences11': 5,\n",
       " 'Geology12': 1,\n",
       " 'History12': 6,\n",
       " 'French9Enriched': 0,\n",
       " 'FoundationsofMathPreCalc.10': 3,\n",
       " 'SocialStudies9': 6,\n",
       " 'BiologyAnatomyPhysiology12': 5}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Computes and displays the final schedule:\n",
    "'''\n",
    "\n",
    "#Finding the chromatic number:\n",
    "chro_num = gp.chromatic_number(G)\n",
    "\n",
    "#The final solution will go into these lists:\n",
    "final_solution_AM = []\n",
    "final_solution_PM = []\n",
    "\n",
    "#This variable is for the while loop below. If it is set to True, the loop will run.\n",
    "#If it is set to False by the time the loop is completed, that means a working solution has been found.\n",
    "#If it is set to True by the time the loop is completed, a solution was not found, and the loop will run again.\n",
    "run = True\n",
    "\n",
    "#This loop applies the random-greedy coloring algorithm until all conditions are met and a solution is found:\n",
    "while run == True:\n",
    "    \n",
    "    #Resetting variables:\n",
    "    final_solution_AM = []\n",
    "    final_solution_PM = []\n",
    "    run = False\n",
    "    \n",
    "    #Tries a random-greedy algorithm for coloring the graph:\n",
    "    color = nx.greedy_color(G, strategy = 'random_sequential')\n",
    "\n",
    "    #Checks that generated coloring has chromatic number of colors, sets loop to run again if it doesn't:\n",
    "    for key in color:\n",
    "        if color[key] >= chro_num:\n",
    "            run = True\n",
    "    \n",
    "    #Checks that no single color has more than 300 students total taking an exam, sets loop to run again if failed:\n",
    "    if run == False:\n",
    "        \n",
    "        student_count = []\n",
    "        \n",
    "        #Sets up student_count to be the appropriate size:\n",
    "        for x in range(0, chro_num):\n",
    "            student_count.append(0)\n",
    "            \n",
    "        for key in color:            \n",
    "            student_count[color[key]] += class_dictionary[key]\n",
    "            \n",
    "        for x in student_count:\n",
    "            if x >= 300:\n",
    "                run = True\n",
    "    \n",
    "    \n",
    "    #Tries to fill the AM/PM slots so that no more than 150 students are writing at a time:\n",
    "    if run == False:\n",
    "        \n",
    "        #Setting up counts for the number of students in the gym during each time slot:\n",
    "        student_count_AM = []\n",
    "        student_count_PM = []\n",
    "        \n",
    "        #Setting up lists of classes for the solution (AM and PM slots), as well as the student count lists\n",
    "        # so that they have one entry for every day.\n",
    "        #The index will represent the day number (eg. the first set of classes will be for the first day, etc.)\n",
    "        for x in range(0, chro_num):\n",
    "            final_solution_AM.append([])\n",
    "            final_solution_PM.append([])\n",
    "            student_count_AM.append(0)\n",
    "            student_count_PM.append(0)\n",
    "        \n",
    "        #Iterates through all the classes on a given day (or a given color),\n",
    "        # in order to sort them into the AM and PM sessions:\n",
    "        for x in range(0, chro_num): \n",
    "            for key in color:\n",
    "                if color[key] == x:\n",
    "                    \n",
    "                    #Adds the class to the solution in the AM slot, if there is still room for it (less than 150 students):\n",
    "                    if student_count_AM[x] + class_dictionary[key] <= 150:\n",
    "                        \n",
    "                        student_count_AM[x] += class_dictionary[key]\n",
    "                        final_solution_AM[x].append(key)\n",
    "                    \n",
    "                    #If the AM session is full, the class is added to the PM slot:\n",
    "                    else:\n",
    "                        student_count_PM[x] += class_dictionary[key]\n",
    "                        final_solution_PM[x].append(key)\n",
    "            \n",
    "            #If the PM session now has more than 150 students, this solution doesn't work, and the loop resets to try again:\n",
    "            if student_count_PM[x] > 150:\n",
    "                run = True            \n",
    "            \n",
    "color\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Students</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th>Session</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Day 1: 146 / 104 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>Science10</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>Calculus12</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>BiologyLifeSciences11Enriched</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>French9</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>FoundationsofMathematics12</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>Science10Enriched</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>FoundationsofMathematics11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>French9Enriched</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Day 2: 125 / 69 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>Physics11</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>Physics12</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>Geology12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>Mathematics9</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Day 3: 133 / 124 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>Chemistry11Enriched</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>Chemistry11</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>Chemistry12</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>Science9</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Day 4: 144 / 127 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>PreCalculus12</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>PreCalculus11</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>PreCalculus11Accelerated</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>PreCalculus12Accelerated</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>FoundationsofMathPreCalc.10</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Day 5: 98 / 57 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>SocialStudies11</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>Spanish9</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>Law12</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Day 6: 141 / 43 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>English9</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>BiologyLifeSciences11</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>BiologyAnatomyPhysiology12</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Day 7: 48 / 124 students</th>\n",
       "      <th>AM</th>\n",
       "      <td>History12</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>SocialStudies9</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           Class  Students\n",
       "Day                       Session                                         \n",
       "Day 1: 146 / 104 students AM                           Science10        63\n",
       "                          AM                          Calculus12        71\n",
       "                          AM       BiologyLifeSciences11Enriched        12\n",
       "                          PM                             French9        35\n",
       "                          PM          FoundationsofMathematics12        18\n",
       "                          PM                   Science10Enriched        22\n",
       "                          PM          FoundationsofMathematics11        12\n",
       "                          PM                     French9Enriched        17\n",
       "Day 2: 125 / 69 students  AM                           Physics11        77\n",
       "                          AM                           Physics12        34\n",
       "                          AM                           Geology12        14\n",
       "                          PM                        Mathematics9        69\n",
       "Day 3: 133 / 124 students AM                 Chemistry11Enriched        56\n",
       "                          AM                         Chemistry11        25\n",
       "                          AM                         Chemistry12        52\n",
       "                          PM                            Science9       124\n",
       "Day 4: 144 / 127 students AM                       PreCalculus12        54\n",
       "                          AM                       PreCalculus11        69\n",
       "                          AM            PreCalculus11Accelerated        21\n",
       "                          PM            PreCalculus12Accelerated        18\n",
       "                          PM         FoundationsofMathPreCalc.10       109\n",
       "Day 5: 98 / 57 students   AM                     SocialStudies11        40\n",
       "                          AM                            Spanish9        58\n",
       "                          PM                               Law12        57\n",
       "Day 6: 141 / 43 students  AM                            English9       117\n",
       "                          AM               BiologyLifeSciences11        24\n",
       "                          PM          BiologyAnatomyPhysiology12        43\n",
       "Day 7: 48 / 124 students  AM                           History12        48\n",
       "                          PM                      SocialStudies9       124"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The rest of the code puts the schedule into a pandas dataframe, and applies some formatting:    \n",
    "day_col = []\n",
    "session_col = []\n",
    "class_col = []\n",
    "students_col = []\n",
    "\n",
    "for day in range(0, chro_num):\n",
    "    \n",
    "    for class_ in final_solution_AM[day]:\n",
    "        day_col.append(\"Day \" + str(day+1) + \": \" + str(student_count_AM[day]) + \" / \" + str(student_count_PM[day]) + \" students\")\n",
    "        session_col.append(\"AM\")\n",
    "        class_col.append(class_)\n",
    "        students_col.append(class_dictionary[class_])\n",
    "    \n",
    "    for class_ in final_solution_PM[day]:\n",
    "        day_col.append(\"Day \" + str(day+1) + \": \" + str(student_count_AM[day]) + \" / \" + str(student_count_PM[day]) + \" students\")\n",
    "        session_col.append(\"PM\")\n",
    "        class_col.append(class_)\n",
    "        students_col.append(class_dictionary[class_])\n",
    "\n",
    "d = {\n",
    "    'Day': day_col,\n",
    "    'Session': session_col,\n",
    "    'Class': class_col,     \n",
    "    'Students': students_col}\n",
    "\n",
    "df = pd.DataFrame(d,columns=['Day','Session','Class','Students'])\n",
    "df1=df.set_index(['Day', 'Session'])\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banners_Bottom_06.06.18.jpg?raw=true)"
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